{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def rmse(y_train, y_pred):\n",
    "    return np.sqrt(metrics.mean_squared_error(y_train, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\pandas\\core\\indexing.py:140: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "0.5807429703552129 4.418\n",
      "0.5807686669960418 4.419\n",
      "0.5807780124654051 4.42\n",
      "0.5808060528309866 4.422\n",
      "0.5808270870019426 4.423\n",
      "0.5808948865088659 4.426\n",
      "0.5809229516328281 4.427\n",
      "0.5809440043792611 4.428\n",
      "0.5810493183367178 4.429\n",
      "0.5811055200388868 4.43\n",
      "0.5811195741913457 4.431\n",
      "0.5811547160979615 4.432\n",
      "0.581178149216489 4.433\n",
      "0.5811945548664164 4.434\n",
      "0.5812015864813072 4.435\n",
      "0.5812766136084184 4.436\n",
      "0.5813493366844318 4.437\n",
      "0.5813587232159468 4.438\n",
      "0.5813751512467117 4.44\n",
      "0.5814080134214294 4.441\n",
      "0.58141975188834 4.442\n",
      "0.5814361874896833 4.443\n",
      "0.5814855065320327 4.444\n",
      "0.5815395437016134 4.446\n",
      "0.5815677457598954 4.447\n",
      "0.5815889012441862 4.448\n",
      "0.5816241678936444 4.449\n",
      "0.5816570919052949 4.45\n",
      "0.5816970820652307 4.451\n",
      "0.5817441447713231 4.453\n",
      "0.581755913060445 4.454\n",
      "0.5817653284444224 4.455\n",
      "0.5817888698320945 4.457\n",
      "0.5818194798908052 4.458\n",
      "0.5818712976444935 4.459\n",
      "0.5818736534785167 4.461\n",
      "0.5818901454896346 4.469\n",
      "0.5818948578698758 4.47\n",
      "0.5819066395537789 4.471\n",
      "0.5819254924273485 4.472\n",
      "0.5819349198701727 4.475\n",
      "0.5819396338431352 4.477\n",
      "0.5819679212033484 4.478\n",
      "0.5820009307580105 4.479\n",
      "0.5820292312086591 4.48\n",
      "0.5820339485381438 4.483\n",
      "0.5821070886287147 4.484\n",
      "0.5821141688449418 4.485\n"
     ]
    }
   ],
   "source": [
    "train_result = pd.read_csv('../result/train_0.6056817644422253.csv')\n",
    "\n",
    "# find 4\n",
    "maxv = 0\n",
    "best_threshold = 0\n",
    "for threshold in range(4000, 5001):\n",
    "    threshold = threshold / 1000\n",
    "    train = train_result.copy()\n",
    "    train['Predict'].ix[(train['Predict'] >= 4.0) & (train['Predict'] < threshold)] = 4\n",
    "    train['Predict'].ix[(train['Predict'] >= threshold)] = 5\n",
    "    error = rmse(train['Predict'].values, train['Score'].values)\n",
    "    error = 1 / (1 + error)\n",
    "    if error > maxv:\n",
    "        best_threshold = threshold\n",
    "        maxv = error\n",
    "        print(maxv, threshold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        ridge_doufu\n",
      "count  30000.000000\n",
      "mean       4.461413\n",
      "std        0.139747\n",
      "min        2.294843\n",
      "25%        4.411634\n",
      "50%        4.469178\n",
      "75%        4.527283\n",
      "max        5.000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\administrator\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\pandas\\core\\indexing.py:140: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n"
     ]
    }
   ],
   "source": [
    "def filter_abnormal(filename, feature = 'Score'):\n",
    "    result = pd.read_csv(filename)\n",
    "    result[feature].ix[result[feature] > 5] = 5\n",
    "    result[feature].ix[result[feature] < 1] = 1\n",
    "    print(result.describe())\n",
    "    return result\n",
    "\n",
    "\n",
    "result = filter_abnormal('../models/__models__/test_ridge_doufu.csv', feature='ridge_doufu')\n",
    "# result.to_csv('../models/__models__/test_ridge_noNormal.csv', index = False)\n",
    "\n",
    "# result = filter_abnormal('../models/__models__/train_ridge_noNormal.csv', feature='ridge_noNormal')\n",
    "# result.to_csv('../models/__models__/train_ridge_noNormal.csv', index = False)\n",
    "\n",
    "# result = filter_abnormal('../models/__models__/test_ridge_withNormal.csv', feature='ridge_withNormal')\n",
    "# result.to_csv('../models/__models__/test_ridge_withNormal.csv', index = False)\n",
    "\n",
    "# result = filter_abnormal('../models/__models__/train_ridge_withNormal.csv', feature='ridge_withNormal')\n",
    "# result.to_csv('../models/__models__/train_ridge_withNormal.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ridge_doufu</th>\n",
       "    </tr>\n",
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       "    <tr>\n",
       "      <th>count</th>\n",
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      "text/plain": [
       "        ridge_doufu\n",
       "count  30000.000000\n",
       "mean       4.461490\n",
       "std        0.140094\n",
       "min        2.294843\n",
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     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgbm_test_100 = pd.read_csv('../models/__models__/test_lasso_noNormal.csv')\n",
    "lgbm_test_100.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
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